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Google Bigquery

Introduction

Google Cloud's Blockchain Analytics offers indexed blockchain data made available through BigQuery for easy analysis through SQL.

Visit the Google Web3 portal to retrieve all available datasets.

Blockchain Analytics offers you access to reliable data without the overhead of operating nodes or developing and maintaining an indexer. You can now query the full history of blocks, transactions, logs and receipts for Cronos.

By leveraging datasets in BigQuery, you can access blockchain data as easily as your internal data. By joining chain data with application data, you can get a complete picture of your users and your business.

How are these datasets different from the existing public dataset?

Like the existing public blockchain datasets, customers are not charged for storage of the data, only for querying the data based on BigQuery pricing.

Quickstart

  1. Go to Cronos dataset and click on one of the samples.
  2. You will get to the console and see the Cronos dataset on the left in the explorer

{% hint style="warning" %} Please note
BigQuery charges are based on the amount of data processed by your queries, so running the query may incur charges to your account. You can find the consumption estimate in the top right corner similar to the warning of "This query will process 65.73 MB when run." {% endhint %}

  1. If you see on the sample you should get the BigQuery SQL code to query:
    Which wallets had the most number of interactions with the Wrapped Cronos contract in the past 30 days?

    Let's click the big RUN button.
    (To save costs, replace the existing query with the one below, using a "1 day" interval instead of "30 days" in the BigQuery console).

    To start developing your own BigQuery SQL code, we refer to the following syntax.
    For the Cronos data schema we refer to the Google Cloud Cronos schema. 

SELECT
 t.from_address AS address,
 CONCAT("https://cronoscan.com/address/", t.from_address) AS cronoscan_link,
 COUNT(t.from_address) AS num_transactions
FROM
 `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions` AS t
INNER JOIN
 bigquery-public-data.goog_blockchain_cronos_mainnet_us.blocks AS b
ON
 b.block_hash = t.block_hash
WHERE
 t.to_address = LOWER("0x5C7F8A570d578ED84E63fdFA7b1eE72dEae1AE23") -- Wrapped CRO
AND
 b.block_timestamp > (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR)
AND
 t.block_timestamp > (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR)
GROUP BY
 t.from_address
ORDER BY
 COUNT(t.from_address) DESC
;
  1. We can now query the results in the results tab below, further explore by exporting the results or visualizing in another tool such as Google sheets or Looker.
Row address cronoscan_link num_transactions
1 0x3270c9a4558774cc8f2a19708edc190366028b96 https://cronoscan.com/address/0x3270c9a4558774cc8f2a19708edc190366028b96 1
2 0xbeaf1c7fed452be2dcfd2a8fe1fcd74b241acfc7 https://cronoscan.com/address/0x693fb96fdda3c382fde7f43a622209c3dd028b98 1
3 0xddb162b31f562f1be0fa585d3ca6a55786e59af3 https://cronoscan.com/address/0x6614d26064d762922c7bc7a00337713d5169ae7c 1

Example queries

1. Latest indexed block

SELECT
  MIN(block_number) AS `First block`,
  MAX(block_number) AS `Newest block`,
  COUNT(1) AS `Total number of blocks`
FROM
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.blocks` AS t
Row First block Newest block Total number of blocks
1 1 12134627 12134627

2. Daily transactions in the last 10 days

SELECT
  DATE(block_timestamp) AS date,
  COUNT(*) AS num_transactions
FROM
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions`
WHERE
  block_timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 10 DAY)
GROUP BY
  1
ORDER BY
  1 DESC;
Rowdatenum_transactions
12024-01-1810250
22024-01-1747747
32024-01-1649717
42024-01-1547099
52024-01-1447051

3. View the blocks with largest CRO value transfer in the past hour

SELECT block_hash, SUM(value.bignumeric_value / 1000000000000000000) value_total
FROM `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions` AS t
 JOIN `bigquery-public-data.goog_blockchain_cronos_mainnet_us.receipts` AS r USING (block_hash, transaction_hash)
WHERE status = 1
AND
 t.block_timestamp > (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR)
AND
 r.block_timestamp > (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR)
GROUP BY block_hash
ORDER BY value_total DESC
LIMIT 5
Rowblock_hashvalue_total
10x7cac0bbf3909902a8670f962fbc9721391178850a2672d82b75c5a79b332a4f836836.840925000000000001
20xb53bd2c1a1d136e9b4dda4af47c488d278a0ee450adecd991cf13b187ce17a9336729
30x28e0d8c31625ca43b565f1202b91e6cb20b709e25bea65185dcda7a3d176957d33359.85627
40xfe8e732779101854cfaeed2404f7b53d3b64879069b5d8e8372f64d1cfe4a47f22577.325273500000000015
50xd0b81821a57dc939dce80b29e9642f4a39e4bd2d346b5943a2532e69f191de5722231.469422733370862931

4. Top 10 wallets by number of transactions in the last hour

SELECT
 from_address,
 COUNT(*) AS num_transactions
FROM `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions` as t
WHERE
t.block_timestamp > (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR)
GROUP BY from_address
ORDER BY num_transactions DESC
LIMIT 10;
Rowfrom_addressnum_transactions
10x25aa97464f38a1506a16160bbc03cfc6dd863da3211
20x227f6757289a86c13eee2e91c2e6eb03f2ed11a6136
30x95d49a8a2d69b2a2de4a00655d05ee39f9c41108134
40x15d190dd8a1ed39cf5b790e2ffed1f365e9c865b116
50xc9219731adfa70645be14cd5d30507266f2092c580
60x6614d26064d762922c7bc7a00337713d5169ae7c65
70x34cfa46732692ab062f0453036cd5a4f5b77147359
80x693fb96fdda3c382fde7f43a622209c3dd028b9850
90x71f0cdb17454ad7eeb7e26242292fe0e0189645a50
100x518a9d51ba8841046859a7722e75f92ffdadd0c437

5. All USDT activity in the past hour

-- UDF for easier string manipulation.
CREATE TEMP FUNCTION ParseSubStr(hexStr STRING, startIndex INT64, endIndex INT64)
RETURNS STRING
LANGUAGE js
AS r"""
 if (hexStr.length < 1) {
   return hexStr;
 }
 return hexStr.substring(startIndex, endIndex);
""";
-- UDF to convert hex to decimal.
CREATE TEMP FUNCTION HexToDecimal(hexStr STRING)
RETURNS INT64
LANGUAGE js
AS r"""
 return parseInt(hexStr, 16);
""";

SELECT
 t.transaction_hash,
 t.from_address AS from_address,
 CONCAT("0x", ParseSubStr(l.topics[OFFSET(2)], 26, LENGTH(l.topics[OFFSET(2)]))) AS to_address,
 (HexToDecimal(l.data) / 1000000) AS usdt_transfer_amount
FROM
 `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions` AS t
INNER JOIN
 `bigquery-public-data.goog_blockchain_cronos_mainnet_us.logs` AS l
ON
 l.transaction_hash = t.transaction_hash
WHERE
 t.to_address = LOWER("0x66e428c3f67a68878562e79a0234c1f83c208770") -- USDT
AND
 t.block_timestamp >= (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR) 
AND
 l.block_timestamp >= (CURRENT_TIMESTAMP() - INTERVAL 1 HOUR) 
AND
 ARRAY_LENGTH(l.topics) > 0
AND
 -- Transfer(address indexed src, address indexed dst, uint wad)
 l.topics[OFFSET(0)] = LOWER("0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef") -- Transfer
;
Rowtransaction_hashfrom_addressto_addressusdt_transfer_amount
10xa3b78f79dee6970f3abc763b52f24a6d46aeba3e2370943f8eb2d68ff00d788a0xc9219731adfa70645be14cd5d30507266f2092c50x539b85a6853e8740cd918009197c799d205787eb309.82
20xc2abd163669a703ee850e432ac7c1a744b63bb1ff8e9b4cdee3ec2d95768fc750xc9219731adfa70645be14cd5d30507266f2092c50xc041126c1d07b72ee0f366e1fca339b4fed537cb9.38
30x214cdef8263b4f8cc517d09673c126375c682b9b83d55c7ad889055c575333900xc9219731adfa70645be14cd5d30507266f2092c50xfca38e2882d8a549c660ccb17a8fe0463fab060e11.82
40x40e24cc93abc746aa7c96482144772421412b9a733210644bab6ff6290996a1e0xc9219731adfa70645be14cd5d30507266f2092c50x67b652172633b451a826aac6da7ed63693133fd211.26
50xb211dcb87ec8bbb115a4c6eae6c5a8861e04557b8b502e5a5858e70ae512183b0x539b85a6853e8740cd918009197c799d205787eb0x8995909dc0960fc9c75b6031d683124a4016825b309.82

6. For Dapps - Count the total number of unique transactions and users interaction with a specific smart contract on a given day 

SELECT
 COUNT(DISTINCT transaction_hash) AS total_transactions,
 COUNT(DISTINCT from_address) AS unique_users,date(block_Timestamp) as date
FROM
`bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions`
WHERE
 to_address = '0x9fae23a2700feecd5b93e43fdbc03c76aa7c08a6'
 AND block_timestamp BETWEEN TIMESTAMP('2023-01-01 00:00:00') AND TIMESTAMP('2023-01-02 00:00:00')
GROUP BY
date
Row
Rowtotal_transactionsunique_usersdate
12881312023-01-01

7. [DApp] Top 5 addresses with the highest number of transactions sent to the contract at a specific contract within the specified date range

SELECT
 from_address,
 COUNT(transaction_hash) AS transaction_count,
FROM
 `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions`
WHERE
 to_address = '0x9fae23a2700feecd5b93e43fdbc03c76aa7c08a6'
 AND block_timestamp BETWEEN TIMESTAMP('2024-05-01 00:00:00 UTC') AND TIMESTAMP('2024-07-31 23:59:59 UTC')
GROUP BY
 from_address
ORDER BY
 transaction_count DESC
LIMIT 5
Rowfrom_addresstransaction_count
10x8c0ec5772bd92d55edd1325d022ec07e54ae1b0e3515
20x849c34e2bcd65a861dfaefe415aafb2826c46b96224
30xc9219731adfa70645be14cd5d30507266f2092c5129
40xfe5b12a84019dcd8178a4c684885a8e685bef23872
50x254b5fed7b453831e89e566a2e02678636e9f01046